1. Field of the Invention
The present invention relates to a medical image processing apparatus and a medical image processing method that assist a diagnosis of an anatomic abnormality, e.g., a nodular abnormality or a wen abnormality, based on a three-dimensional image collected by using a medical diagnostic imaging modality, e.g., an X-ray computer tomographic apparatus, an X-ray diagnostic apparatus, a magnetic resonance diagnostic apparatus, or an ultrasonic diagnostic apparatus.
2. Description of the Related Art
At the present day, a lung cancer heads a list of malignant deaths and goes on increasing in Japan. Therefore, a social demand for early detection is strong with respect to the lung cancer like precaution as a countermeasure for smoking. In each municipalities in Japan, a lung cancer examination based on a chest plain radiograph and a sputum cytodiagnosis is carried out. However, a report “Study Group Concerning Cancer Examination Effectiveness Evaluation” issued from Health and Welfare Ministry in Japan in 1998 concludes that a current lung cancer examination has effectiveness but it is small. An X-ray computer tomography (which will be referred to as a CT hereinafter) can readily detect a lung field type lung cancer as compared with a chest plain radiograph, but it was not able to be used for examination since its imaging time is long before 1990 when a helical scanning type CT (helical CT) appeared. However, soon after the helical CT appeared, a method of using a relatively low X-ray tube current to perform imaging for a reduction in radiation exposure (which will be referred to as a low-dose helical CT hereinafter) was developed, and a pilot study of a lung cancer examination using this method was carried out in Japan and the United States. As a result, a fact that the low-dose helical CT has a lung cancer detection rate greatly higher than that of the chest plain radiograph was proved.
On the other hand, a time required for imaging by the helical CT is kept being reduced due to an increase CT detectors after 1998. The latest multi-detector helical CT, an entire lung can be imaged in 10 seconds with a substantially isotropic resolution that is less than 1 mm. Such a CT technology innovation develops a potentiality of enabling detection of a lung cancer when it is smaller. However, the multi-detector helical CT also has a problem of considerably increasing a burden on diagnosing reading since it generates several-hundreds images per scanning operation.
Based on such a background, it is widely recognized that a computer assisted diagnosis (which will be referred to as a CAD hereinafter) using a computer to avoid an oversight of a lung cancer is required for the low-dose helical CT to be established as a lung cancer examination method.
Since a small lung cancer in a lung field appears as a nodular abnormality in a CT image, automatic detection of such an abnormality is an important theme, and various studies have been conducted since the 1990's (see, e.g., “David S. Paik and seven others, “Surface Normal Overlap: A Computer-aided Detection Algorithm with Application to Colonic Polyps and Lung Nodules in Helical CT”, IEEE Transactions on Medical Imaging, Vol. 23, No. 6, Jun. 2004, pp. 661-675”).
Now, it has been reported that many nodule candidates detected by a CAD are benign. Therefore, several techniques have been introduced because it is recognized that computer-aided differential diagnosis for revealing whether a nodule candidate is benign or malignant is also important (see, e.g., Kenji Suzuki and three others, “Computer-Aided Diagnostic Scheme for Distinction Between Benign and Malignant Nodules in Thoracic Low-dose CT by Use of Massive Training Artificial Neural Network”, IEEE TRANSACTION ON MEDICAL IMAGING, VOL. 24, NO. 9, Sept. 2005, pp. 1138-1150).
Automatic detection of a pulmonary nodule in a CT image takes an approach of extracting a region as a candidate for a nodule (which will be referred to as a nodule candidate region hereinafter) by any method, obtaining a plurality of characteristic amounts characterizing this nodule candidate region, and determining whether the nodule candidate region is a nodule based on these characteristic amounts. However, since characteristics of a nodule are similar to those of a part of a lung blood vessel, the nodule and the lung blood vessel cannot be accurately discriminated from each other based on characteristic amounts charactering the nodule candidate region in some cases.
Under such circumstances, a final judgment on whether the nodule candidate region is a nodule is still committed to medical doctors. Usually, this judgment is made based on observation of an image showing a cross section. However, in this method, since a three-dimensional shape cannot be immediately recognized discriminating the nodule from the lung blood vessel takes time in some cases, and it cannot be said that an efficiency of the judgment is necessarily good.
As explained above, an examination policy or a treatment policy concerning an abnormality candidate region has been conventionally determined based on a physician's personal opinions, and hence it is not necessarily determined adequately.